Let's first turn on the cache for increased performance and improved styling

# Set some global knitr options
library("knitr")
opts_chunk$set(tidy=TRUE, tidy.opts=list(blank=FALSE, width.cutoff=60), cache=TRUE, messages=FALSE)

• Motivation
• Reverse transcription and hybridization cDNA microarrays
• Oligonucleotide arrays (Affymetrix)
• Illumina beadarrays

• Why are microarrays so important?
• First technology to enable the genomewide quantification of gene-expression
• Characterize and classify diseases, design new drugs, etc.
• "Recent" technology (1995,1996)
• Fast moving field

• Isolate mRNA from cells
• From RNA we can get DNA using an enzyme called reverse transcriptase (e.g. Retrovirus)
• The derived "copy" DNA (cDNA) is hybridized to known DNA targets (genes) to quantify gene expression

Hybridization

• Fabricated by placing short cDNA sequences (oligonucleotides) on a small silicon chip by a photolithographic process
• Each gene is represented by a set of distinct probes, cDNA segments of length 25 nucleotides (11-20)
• Probes chosen based on uniqueness criteria and empirical rules
• Single color

• about 20 probes that “perfectly” represent the gene (Perfect Match)

• about 20 probes that do not match the gene sequence (Mismatch)

• Probeset

For a valid gene expression measurement the Perfect Match sticks and the Mistach does not!

Let's watch a small video

http://www.youtube.com/watch?v=ui4BOtwJEXs

(Source: http://www.mouseclinic.de/)

• Illumina bead arrays can be applied to different problems
• Can use one or two colors depending on the application
• Gene expression arrays use one color

• High density arrays
  • HumanHT-12 v4.0 Expression BeadChip, about 47,000 probes
• A specific oligonucleotide sequence is assigned to each bead type, which is replicated about 30 times on an array
• Bead level data can be relavitely large

1. Biological question
2. Experimental design
3. Experiment
4. Image analysis
5. Normalization, Batch effect removal
6. Estimation, Testing, Clustering, Prediction, Classification, Feature selection, etc.
7. Validate finding generate new hypothesis -> New experiments

Most of these steps require the use of statistical methods and/or computational tools

Note: Even though microarrays were designed to study gene expression, they can be used to study many other things (DNA-protein binding, methylation, splicing, etc). Though they are slowly being replaced by sequencing-based assays.